QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
This addresses the problem of limited quantum computing capabilities for machine learning tasks, though it is incremental in improving performance on near-term hardware.
The researchers tackled the challenge of performing multi-class classification on current error-prone quantum computers by developing the QUILT framework, achieving up to 85% accuracy on the MNIST dataset with a five-qubit system.
Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.